Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2269515
S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi
Heart disease is a leading cause of mortality and illness worldwide. Heart disease identification and prediction may considerably improve patient outcomes. We use deep neural networks (DNNs) and heart rate variability (HRV) data to construct a deep learning strategy for diagnosing cardiovascular abnormalities in diabetic men. The non-invasive HRV test shows how the autonomic nervous system affects heart function. It show promise for diagnosing heart dysfunction. DNNs, noted for their ability to interpret complex data patterns, are useful for prediction and diagnosis. Our unique system, DNHRV (Deep Neural Network with HRV Features), integrates two networks using DNN and DCNN methods (Deep Convolutional Neural Network). Our DNN analyses clinical risk variables using powerful deep learning architecture, while the DCNN trains. We integrate HRV signals, medical pictures, and other clinical parameters with deep neural network computing power in the suggested technique (DNNs). This multimodal technique gives us a complete picture of each patient's cardiovascular health by utilising physiological and imaging-based indicators. Our DNHRV model outperformed earlier models in accuracy, precision, F1-score, and other parameters. Our prediction model was evaluated using SHAREEDB, proving its accuracy and stability. The DNHRV model exceeds state-of-the-art CVD prediction methods by a large margin, with 98.8% accuracy, according to extensive SHAREEDB dataset tests. By highlighting CVD predicting data points, the suggested technique increased interpretability and accuracy.
{"title":"A multi-modal integrated deep neural networks for the prediction of cardiovascular disease in type-2 diabetic males","authors":"S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi","doi":"10.1080/00051144.2023.2269515","DOIUrl":"https://doi.org/10.1080/00051144.2023.2269515","url":null,"abstract":"Heart disease is a leading cause of mortality and illness worldwide. Heart disease identification and prediction may considerably improve patient outcomes. We use deep neural networks (DNNs) and heart rate variability (HRV) data to construct a deep learning strategy for diagnosing cardiovascular abnormalities in diabetic men. The non-invasive HRV test shows how the autonomic nervous system affects heart function. It show promise for diagnosing heart dysfunction. DNNs, noted for their ability to interpret complex data patterns, are useful for prediction and diagnosis. Our unique system, DNHRV (Deep Neural Network with HRV Features), integrates two networks using DNN and DCNN methods (Deep Convolutional Neural Network). Our DNN analyses clinical risk variables using powerful deep learning architecture, while the DCNN trains. We integrate HRV signals, medical pictures, and other clinical parameters with deep neural network computing power in the suggested technique (DNNs). This multimodal technique gives us a complete picture of each patient's cardiovascular health by utilising physiological and imaging-based indicators. Our DNHRV model outperformed earlier models in accuracy, precision, F1-score, and other parameters. Our prediction model was evaluated using SHAREEDB, proving its accuracy and stability. The DNHRV model exceeds state-of-the-art CVD prediction methods by a large margin, with 98.8% accuracy, according to extensive SHAREEDB dataset tests. By highlighting CVD predicting data points, the suggested technique increased interpretability and accuracy.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2262016
Qixiang Wang, Fei Long, Lipo Mo, Jing Yang
In this paper, we address the almost sure stability problem of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals (DS-CFLSs). Firstly, due to the non-locality and memory of fractional-order switched systems, an inequality is proposed to solve the difficulties in the discussion of stability. Then, for DS-CFLSs, a deterministic switching strategy is predesigned, and stochastic switching signals are generated by the Markov process. After that, for the globally asymptotic stability almost surely (GAS a.s.) and exponential stability almost surely (ES a.s.) of DS-CFLSs, some sufficient conditions are proposed by using the multi-Lyapunov function and probability analysis methods. Finally, some numerical examples show that our results are effective.
{"title":"Almost sure stability of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals","authors":"Qixiang Wang, Fei Long, Lipo Mo, Jing Yang","doi":"10.1080/00051144.2023.2262016","DOIUrl":"https://doi.org/10.1080/00051144.2023.2262016","url":null,"abstract":"In this paper, we address the almost sure stability problem of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals (DS-CFLSs). Firstly, due to the non-locality and memory of fractional-order switched systems, an inequality is proposed to solve the difficulties in the discussion of stability. Then, for DS-CFLSs, a deterministic switching strategy is predesigned, and stochastic switching signals are generated by the Markov process. After that, for the globally asymptotic stability almost surely (GAS a.s.) and exponential stability almost surely (ES a.s.) of DS-CFLSs, some sufficient conditions are proposed by using the multi-Lyapunov function and probability analysis methods. Finally, some numerical examples show that our results are effective.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2241772
J. Jayalakshmi, M. Mary Linda
WPREs (wind power ramp events) are one of the most critical factors affecting the security and protection of the electrical system. Accurate ramp event detection may help power systems better manage extreme events and reduce financial damage. In this study, We present an improved piecewise linear approximation for recognizing wind ramps in Kanyakumari district. In practise, wind power ramps can be decreased by properly managing and dispatching flexible reserve and associated services. This necessitates the use of proper ramp detection techniques as well as precise ramp forecasts. The method’s plan to break down wind power signal into increasing with increasing ramps, making ramp identification easier and ensuring that all conceivable ramps of varying lengths are identified. Using observed wind power data, the ramp detection method is used to assess the performance of an energy wind farm. The results reveal that identifying wind power ramps using the segmentation method is equivalent to optical ramp identification.
{"title":"Piecewise linear approximation for identifying wind power ramp events","authors":"J. Jayalakshmi, M. Mary Linda","doi":"10.1080/00051144.2023.2241772","DOIUrl":"https://doi.org/10.1080/00051144.2023.2241772","url":null,"abstract":"WPREs (wind power ramp events) are one of the most critical factors affecting the security and protection of the electrical system. Accurate ramp event detection may help power systems better manage extreme events and reduce financial damage. In this study, We present an improved piecewise linear approximation for recognizing wind ramps in Kanyakumari district. In practise, wind power ramps can be decreased by properly managing and dispatching flexible reserve and associated services. This necessitates the use of proper ramp detection techniques as well as precise ramp forecasts. The method’s plan to break down wind power signal into increasing with increasing ramps, making ramp identification easier and ensuring that all conceivable ramps of varying lengths are identified. Using observed wind power data, the ramp detection method is used to assess the performance of an energy wind farm. The results reveal that identifying wind power ramps using the segmentation method is equivalent to optical ramp identification.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2261088
T. R. Dinesh Kumar, A. Karthikeyan
3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the “Dynamic Low Power Management Method in 3DWiNoC” (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design.
{"title":"Dynamic low power management technique for decision directed inter-layer communication in three dimensional wireless network on chip","authors":"T. R. Dinesh Kumar, A. Karthikeyan","doi":"10.1080/00051144.2023.2261088","DOIUrl":"https://doi.org/10.1080/00051144.2023.2261088","url":null,"abstract":"3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the “Dynamic Low Power Management Method in 3DWiNoC” (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1080/00051144.2023.2253067
V. Karpagam, N. Narmadhai
An Innovative Modular multilevel matrix converter (M3C) is proposed with reduced number of switching device owing to the improved efficiency, reduced cost and minimizes the size. Offshore Low-Frequency AC (LFAC) transmissions are economical with greater reliability for short and intermediate distance transmissions. Similar to HVDC, it increases the transmission capacity and also distance can be increased in LFAC.M3C is proposed as frequency converters for LFAC transmissions which link AC systems operating at 16.7 and 50 Hz. The double αβ0 transform control technique has been the most often used approach for decoupling control of input, output and circulating currents in such applications. The performances of this work’s proposed modular multilevel matrix converters are analysed using simulation in MATLAB/SIMULINK software.
{"title":"Seven levels highly efficient modular multilevel matrix converter (M3C) for low frequency three-phase AC-AC conversion","authors":"V. Karpagam, N. Narmadhai","doi":"10.1080/00051144.2023.2253067","DOIUrl":"https://doi.org/10.1080/00051144.2023.2253067","url":null,"abstract":"An Innovative Modular multilevel matrix converter (M3C) is proposed with reduced number of switching device owing to the improved efficiency, reduced cost and minimizes the size. Offshore Low-Frequency AC (LFAC) transmissions are economical with greater reliability for short and intermediate distance transmissions. Similar to HVDC, it increases the transmission capacity and also distance can be increased in LFAC.M3C is proposed as frequency converters for LFAC transmissions which link AC systems operating at 16.7 and 50 Hz. The double αβ0 transform control technique has been the most often used approach for decoupling control of input, output and circulating currents in such applications. The performances of this work’s proposed modular multilevel matrix converters are analysed using simulation in MATLAB/SIMULINK software.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136314457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1080/00051144.2023.2256521
V. Nagasumathy, B. Paulchamy
Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation.
{"title":"Detection of glioma on brain MRIs using adaptive segmentation and modified graph neural network based classification","authors":"V. Nagasumathy, B. Paulchamy","doi":"10.1080/00051144.2023.2256521","DOIUrl":"https://doi.org/10.1080/00051144.2023.2256521","url":null,"abstract":"Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/00051144.2023.2244307
S. Balaji, T. Arunprasath, M. Pallikonda Rajasekaran, G. Vishnuvarthanan, K. Sindhuja
Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.
{"title":"Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm","authors":"S. Balaji, T. Arunprasath, M. Pallikonda Rajasekaran, G. Vishnuvarthanan, K. Sindhuja","doi":"10.1080/00051144.2023.2244307","DOIUrl":"https://doi.org/10.1080/00051144.2023.2244307","url":null,"abstract":"Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135063601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/00051144.2023.2246810
Poornima Pandian, Chithra Selvaraj
Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.
{"title":"An incentive-based dynamic energy efficient spectrum allocation for cognitive radio networks","authors":"Poornima Pandian, Chithra Selvaraj","doi":"10.1080/00051144.2023.2246810","DOIUrl":"https://doi.org/10.1080/00051144.2023.2246810","url":null,"abstract":"Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/00051144.2023.2254981
Zhou Zheng
Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.
{"title":"Phase space load balancing priority scheduling algorithm for cloud computing clusters","authors":"Zhou Zheng","doi":"10.1080/00051144.2023.2254981","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254981","url":null,"abstract":"Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/00051144.2023.2254978
Lu Ming
With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.
{"title":"Searchable encryption algorithm in computer big data processing application","authors":"Lu Ming","doi":"10.1080/00051144.2023.2254978","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254978","url":null,"abstract":"With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：